1
|
Abs E, Chase AB, Manzoni S, Ciais P, Allison SD. Microbial evolution-An under-appreciated driver of soil carbon cycling. GLOBAL CHANGE BIOLOGY 2024; 30:e17268. [PMID: 38562029 DOI: 10.1111/gcb.17268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 03/18/2024] [Accepted: 03/18/2024] [Indexed: 04/04/2024]
Abstract
Although substantial advances in predicting the ecological impacts of global change have been made, predictions of the evolutionary impacts have lagged behind. In soil ecosystems, microbes act as the primary energetic drivers of carbon cycling; however, microbes are also capable of evolving on timescales comparable to rates of global change. Given the importance of soil ecosystems in global carbon cycling, we assess the potential impact of microbial evolution on carbon-climate feedbacks in this system. We begin by reviewing the current state of knowledge concerning microbial evolution in response to global change and its specific effect on soil carbon dynamics. Through this integration, we synthesize a roadmap detailing how to integrate microbial evolution into ecosystem biogeochemical models. Specifically, we highlight the importance of microscale mechanistic soil carbon models, including choosing an appropriate evolutionary model (e.g., adaptive dynamics, quantitative genetics), validating model predictions with 'omics' and experimental data, scaling microbial adaptations to ecosystem level processes, and validating with ecosystem-scale measurements. The proposed steps will require significant investment of scientific resources and might require 10-20 years to be fully implemented. However, through the application of multi-scale integrated approaches, we will advance the integration of microbial evolution into predictive understanding of ecosystems, providing clarity on its role and impact within the broader context of environmental change.
Collapse
Affiliation(s)
- Elsa Abs
- Department of Ecology and Evolutionary Biology, University of California, Irvine, Irvine, California, USA
- Laboratoire Des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Alexander B Chase
- Department of Earth Sciences, Southern Methodist University, Dallas, Texas, USA
| | - Stefano Manzoni
- Department of Physical Geography and Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden
| | - Philippe Ciais
- Laboratoire Des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Steven D Allison
- Department of Ecology and Evolutionary Biology, University of California, Irvine, Irvine, California, USA
- Department of Earth System Science, University of California, Irvine, Irvine, California, USA
| |
Collapse
|
2
|
Martiny JBH, Martiny AC, Brodie E, Chase AB, Rodríguez-Verdugo A, Treseder KK, Allison SD. Investigating the eco-evolutionary response of microbiomes to environmental change. Ecol Lett 2023; 26 Suppl 1:S81-S90. [PMID: 36965002 DOI: 10.1111/ele.14209] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 02/13/2023] [Accepted: 03/06/2023] [Indexed: 03/27/2023]
Abstract
Microorganisms are the primary engines of biogeochemical processes and foundational to the provisioning of ecosystem services to human society. Free-living microbial communities (microbiomes) and their functioning are now known to be highly sensitive to environmental change. Given microorganisms' capacity for rapid evolution, evolutionary processes could play a role in this response. Currently, however, few models of biogeochemical processes explicitly consider how microbial evolution will affect biogeochemical responses to environmental change. Here, we propose a conceptual framework for explicitly integrating evolution into microbiome-functioning relationships. We consider how microbiomes respond simultaneously to environmental change via four interrelated processes that affect overall microbiome functioning (physiological acclimation, demography, dispersal and evolution). Recent evidence in both the laboratory and the field suggests that ecological and evolutionary dynamics occur simultaneously within microbiomes; however, the implications for biogeochemistry under environmental change will depend on the timescales over which these processes contribute to a microbiome's response. Over the long term, evolution may play an increasingly important role for microbially driven biogeochemical responses to environmental change, particularly to conditions without recent historical precedent.
Collapse
Affiliation(s)
- Jennifer B H Martiny
- Department of Ecology and Evolutionary Biology, University of California, Irvine, California, USA
| | - Adam C Martiny
- Department of Ecology and Evolutionary Biology, University of California, Irvine, California, USA
- Department of Earth System Science, University of California, Irvine, California, USA
| | - Eoin Brodie
- Earth and Environmental Sciences, Lawrence Berkeley National Laboratory, Berkeley, California, USA
- Department of Environmental Science, Policy, and Management, University of California, Berkeley, California, USA
| | - Alexander B Chase
- Department of Earth Sciences, Southern Methodist University, Dallas, Texas, USA
| | | | - Kathleen K Treseder
- Department of Ecology and Evolutionary Biology, University of California, Irvine, California, USA
| | - Steven D Allison
- Department of Ecology and Evolutionary Biology, University of California, Irvine, California, USA
- Department of Earth System Science, University of California, Irvine, California, USA
| |
Collapse
|
3
|
Sun Y, Goll DS, Huang Y, Ciais P, Wang YP, Bastrikov V, Wang Y. Machine learning for accelerating process-based computation of land biogeochemical cycles. GLOBAL CHANGE BIOLOGY 2023; 29:3221-3234. [PMID: 36762511 DOI: 10.1111/gcb.16623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 01/02/2023] [Indexed: 05/03/2023]
Abstract
Global change ecology nowadays embraces ever-growing large observational datasets (big-data) and complex mathematical models that track hundreds of ecological processes (big-model). The rapid advancement of the big-data-big-model has reached its bottleneck: high computational requirements prevent further development of models that need to be integrated over long time-scales to simulate the distribution of ecosystems carbon and nutrient pools and fluxes. Here, we introduce a machine-learning acceleration (MLA) tool to tackle this grand challenge. We focus on the most resource-consuming step in terrestrial biosphere models (TBMs): the equilibration of biogeochemical cycles (spin-up), a prerequisite that can take up to 98% of the computational time. Through three members of the ORCHIDEE TBM family part of the IPSL Earth System Model, including versions that describe the complex interactions between nitrogen, phosphorus and carbon that do not have any analytical solution for the spin-up, we show that an unoptimized MLA reduced the computation demand by 77%-80% for global studies via interpolating the equilibrated state of biogeochemical variables for a subset of model pixels. Despite small biases in the MLA-derived equilibrium, the resulting impact on the predicted regional carbon balance over recent decades is minor. We expect a one-order of magnitude lower computation demand by optimizing the choices of machine learning algorithms, their settings, and balancing the trade-off between quality of MLA predictions and need for TBM simulations for training data generation and bias reduction. Our tool is agnostic to gridded models (beyond TBMs), compatible with existing spin-up acceleration procedures, and opens the door to a wide variety of future applications, with complex non-linear models benefit most from the computational efficiency.
Collapse
Affiliation(s)
- Yan Sun
- College of Marine Life Sciences, Ocean University of China, Qingdao, China
- Laboratoire des Sciences du Climat et de 1'Environnement, CEA-CNRS-UVSQ, Gif sur Yvette, France
| | - Daniel S Goll
- Laboratoire des Sciences du Climat et de 1'Environnement, CEA-CNRS-UVSQ, Gif sur Yvette, France
| | | | - Philippe Ciais
- Laboratoire des Sciences du Climat et de 1'Environnement, CEA-CNRS-UVSQ, Gif sur Yvette, France
| | | | | | - Yilong Wang
- State Key Laboratory of Tibetan Plateau Earth System, Resources and Environment (TPESRE), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China
| |
Collapse
|
4
|
Manzoni S, Chakrawal A, Ledder G. Decomposition rate as an emergent property of optimal microbial foraging. Front Ecol Evol 2023. [DOI: 10.3389/fevo.2023.1094269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
Decomposition kinetics are fundamental for quantifying carbon and nutrient cycling in terrestrial and aquatic ecosystems. Several theories have been proposed to construct process-based kinetics laws, but most of these theories do not consider that microbial decomposers can adapt to environmental conditions, thereby modulating decomposition. Starting from the assumption that a homogeneous microbial community maximizes its growth rate over the period of decomposition, we formalize decomposition as an optimal control problem where the decomposition rate is a control variable. When maintenance respiration is negligible, we find that the optimal decomposition kinetics scale as the square root of the substrate concentration, resulting in growth kinetics following a Hill function with exponent 1/2 (rather than the Monod growth function). When maintenance respiration is important, optimal decomposition is a more complex function of substrate concentration, which does not decrease to zero as the substrate is depleted. With this optimality-based formulation, a trade-off emerges between microbial carbon-use efficiency (ratio of growth rate over substrate uptake rate) and decomposition rate at the beginning of decomposition. In environments where carbon substrates are easily lost due to abiotic or biotic factors, microbes with higher uptake capacity and lower efficiency are selected, compared to environments where substrates remain available. The proposed optimization framework provides an alternative to purely empirical or process-based formulations for decomposition, allowing exploration of the effects of microbial adaptation on element cycling.
Collapse
|
5
|
Qu R, Liu G, Yue M, Wang G, Peng C, Wang K, Gao X. Soil temperature, microbial biomass and enzyme activity are the critical factors affecting soil respiration in different soil layers in Ziwuling Mountains, China. Front Microbiol 2023; 14:1105723. [PMID: 36876107 PMCID: PMC9978110 DOI: 10.3389/fmicb.2023.1105723] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 01/30/2023] [Indexed: 02/18/2023] Open
Abstract
Soil microorganisms are critical biological indicators for evaluating soil health and play a vital role in carbon (C)-climate feedback. In recent years, the accuracy of models in terms of predicting soil C pools has been improved by considering the involvement of microbes in the decomposition process in ecosystem models, but the parameter values of these models have been assumed by researchers without combining observed data with the models and without calibrating the microbial decomposition models. Here, we conducted an observational experiment from April 2021 to July 2022 in the Ziwuling Mountains, Loess Plateau, China, to explore the main influencing factors of soil respiration (RS) and determine which parameters can be incorporated into microbial decomposition models. The results showed that the RS rate is significantly correlated with soil temperature (TS) and moisture (MS), indicating that TS increases soil C loss. We attributed the non-significant correlation between RS and soil microbial biomass carbon (MBC) to variations in microbial use efficiency, which mitigated ecosystem C loss by reducing the ability of microorganisms to decompose organic resources at high temperatures. The structural equation modeling (SEM) results demonstrated that TS, microbial biomass, and enzyme activity are crucial factors affecting soil microbial activity. Our study revealed the relations between TS, microbial biomass, enzyme activity, and RS, which had important scientific implications for constructing microbial decomposition models that predict soil microbial activity under climate change in the future. To better understand the relationship between soil dynamics and C emissions, it will be necessary to incorporate climate data as well as RS and microbial parameters into microbial decomposition models, which will be important for soil conservation and reducing soil C loss in the Loess Plateau.
Collapse
Affiliation(s)
- Ruosong Qu
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, Northwest University, Xi'an, China.,College of Life Science, Northwest University, Xi'an, China
| | - Guanzhen Liu
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, Northwest University, Xi'an, China.,College of Life Science, Northwest University, Xi'an, China
| | - Ming Yue
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, Northwest University, Xi'an, China.,College of Life Science, Northwest University, Xi'an, China
| | - Gangsheng Wang
- State Key Laboratory of Water Resources and Hydropower Engineering Sciences, Institute for Water-Carbon Cycles and Carbon Neutrality, Wuhan University, Wuhan, China
| | - Changhui Peng
- Department of Biology Sciences, Institute of Environment Sciences, University of Quebec at Montreal, Montreal, QC, Canada
| | - Kefeng Wang
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, Northwest University, Xi'an, China.,College of Life Science, Northwest University, Xi'an, China
| | - Xiaoping Gao
- Shuanglong State-Owned Ecological Experimental Forest Farm of Qiaoshan State-Owned Forestry Administration of Yan'an City, Yan'an, Shaanxi, China
| |
Collapse
|
6
|
van den Berg NI, Machado D, Santos S, Rocha I, Chacón J, Harcombe W, Mitri S, Patil KR. Ecological modelling approaches for predicting emergent properties in microbial communities. Nat Ecol Evol 2022; 6:855-865. [PMID: 35577982 PMCID: PMC7613029 DOI: 10.1038/s41559-022-01746-7] [Citation(s) in RCA: 60] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 03/23/2022] [Indexed: 12/20/2022]
Abstract
Recent studies have brought forward the critical role of emergent properties in shaping microbial communities and the ecosystems of which they are a part. Emergent properties-patterns or functions that cannot be deduced linearly from the properties of the constituent parts-underlie important ecological characteristics such as resilience, niche expansion and spatial self-organization. While it is clear that emergent properties are a consequence of interactions within the community, their non-linear nature makes mathematical modelling imperative for establishing the quantitative link between community structure and function. As the need for conservation and rational modulation of microbial ecosystems is increasingly apparent, so is the consideration of the benefits and limitations of the approaches to model emergent properties. Here we review ecosystem modelling approaches from the viewpoint of emergent properties. We consider the scope, advantages and limitations of Lotka-Volterra, consumer-resource, trait-based, individual-based and genome-scale metabolic models. Future efforts in this research area would benefit from capitalizing on the complementarity between these approaches towards enabling rational modulation of complex microbial ecosystems.
Collapse
Affiliation(s)
| | - Daniel Machado
- Department of Biotechnology and Food Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Sophia Santos
- Centre of Biological Engineering, University of Minho, Braga, Portugal
| | - Isabel Rocha
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal
| | - Jeremy Chacón
- Ecology, Evolution and Behavior, University of Minnesota, Minneapolis, MN, USA
| | - William Harcombe
- Ecology, Evolution and Behavior, University of Minnesota, Minneapolis, MN, USA
| | - Sara Mitri
- Département de Microbiologie Fondamentale, University of Lausanne, Lausanne, Switzerland
| | - Kiran R Patil
- Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, UK.
| |
Collapse
|
7
|
Defrenne CE, Abs E, Longhi Cordeiro A, Dietterich L, Hough M, Jones JM, Kivlin SN, Chen W, Cusack D, Franco ALC, Khasanova A, Stover D, Romero‐Olivares AL. The Ecology Underground coalition: building a collaborative future of belowground ecology and ecologists. THE NEW PHYTOLOGIST 2021; 229:3058-3064. [PMID: 33616944 PMCID: PMC7986216 DOI: 10.1111/nph.17163] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Affiliation(s)
- Camille E. Defrenne
- Climate Change Science Institute and Environmental Sciences DivisionOak Ridge National LaboratoryOak RidgeTN37830USA
| | - Elsa Abs
- Department of Ecology and Evolutionary BiologyUniversity of California Irvine321 SteinhausIrvineCA92697USA
| | - Amanda Longhi Cordeiro
- Department of Ecosystem Science and SustainabilityColorado State UniversityCampus Delivery 1476Fort CollinsCO80523USA
| | - Lee Dietterich
- Department of Ecosystem Science and SustainabilityColorado State UniversityCampus Delivery 1476Fort CollinsCO80523USA
| | - Moira Hough
- Department of Ecology & Evolutionary BiologyUniversity of ArizonaTucsonAZ85721USA
| | - Jennifer M. Jones
- The Kellogg Biological StationMichigan State UniversityHickory CornersMI48824USA
| | - Stephanie N. Kivlin
- Department of Ecology and Evolutionary BiologyUniversity of TennesseeKnoxvilleTN37996USA
| | - Weile Chen
- College of Life SciencesZhejiang UniversityHangzhouZhejiang310027China
| | - Daniela Cusack
- Department of Ecosystem Science and SustainabilityColorado State UniversityCampus Delivery 1476Fort CollinsCO80523USA
| | | | - Albina Khasanova
- Department of Integrative BiologyUniversity of Texas at AustinAustinTX78712USA
| | | | | |
Collapse
|